The Effects of Robot Appearances, Voice Types, and Emotions on Emotion Perception Accuracy and Subjective Perception on Robots

Ko, Sangjin; Liu, Xiaozhen; Mamros, Jake; Lawson, Emily; Swaim, H.W.; Yao, Cheng-Kai; Jeon, Myounghoon

doi:10.1007/978-3-030-60117-1_13

Cited by 5 publications

(4 citation statements)

References 12 publications

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“…The emotion recognition accuracy percentages for anger (37.50%) and sadness (50.00%) were the lowest among the seven emotions. This result was similar to the results from the previous study (Ko et al, 2020), in which negative emotions showed lower recognition accuracy. In the confusion matrix between presented and perceived emotions, anger was mostly misclassified as disgust (34.0%) and sadness was mostly misclassified as anticipation (21.0%).…”

Section: Discussionsupporting

confidence: 92%

“…The result in the accuracy of recognizing emotions in expression types with and without facial expressions recommended recruiting more participants in order to find whether there will be an overall significant difference. A follow-up study with the robot voice used in the previous study (Ko et al, 2020) should be conducted to avoid unnecessary robot features that impact participants’ perceptions toward the emotions. In the future study, gender and age could be considered as factors, particularly because Lawrence, Campbell, and Skuse (2015) stated that gender and age differences may lead to differing emotive facial expression recognition results.…”

Section: Discussionmentioning

confidence: 99%

“…Displaying appropriate facial expressions according to situations is also necessary in human-robot interaction. When the robot has the ability to change its emotions based on the environment and expresses its emotions through facial and whole-body motions, it is perceived as more friendly, human-like, favorable, and available (Ko et al, 2020). In addition, adding facial expressions to a robot improves participants’ emotion recognition ability meaning that people will identify emotions faster and more accurately from the robot with facial expressions (Harwood, Hall, & Shinkfield, 1999).…”

Section: Introductionmentioning

confidence: 99%

“…Additional research has shown that people are more likely to feel comfortable and positive towards humanlike agents rather than robotic counterparts (Broadbent et al, 2013). Studies have further examined the impact of robot type, voice, and emotion on perception of the robot (Ko et al, 2020). From these previous studies, it is indicated that different modalities have diverse influences on how users perceive a robotic agent.…”

Section: Introductionmentioning

confidence: 99%

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Facial Expressions Increase Emotion Recognition Clarity and Improve Warmth and Attractiveness on a Humanoid Robot without Adding the Uncanny Valley

Dong,

Santiago-Anaya,

Jeon

2023

Proceedings of the Human Factors and Ergonomics Society Annual

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With the rising impact of social robots, research on understanding user perceptions and preferences of these robots gains further importance, especially for emotional expressions. However, research on facial expressions and their effects on human-robot interaction has had mixed results. To unpack this issue further, we investigated users’ emotion recognition accuracy and perceptions when interacting with a social robot that displayed emotional facial expressions or not in a storytelling setting. In our experiment, twenty-eight participants received verbal feedback either with or without facial expressions from the robot. Participants showed a significant recognition accuracy effect for emotions and significantly higher clarity for disgust, happiness, and surprise in the facial expression condition than in the no facial expression condition. In addition, participants rated Milo with facial expressions significantly higher than Milo without facial expressions in warmth and attractiveness. No significant differences were found among the rating scores of naturalness. The results from the present study indicated the importance of facial expressions when considering design choices in social robots.

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Section: Discussionsupporting

confidence: 92%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Facial Expressions Increase Emotion Recognition Clarity and Improve Warmth and Attractiveness on a Humanoid Robot without Adding the Uncanny Valley

Dong,

Santiago-Anaya,

Jeon

2023

Proceedings of the Human Factors and Ergonomics Society Annual

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Theory of Mind Assessment with Human-Human and Human-Robot Interactions

Rabe,

Callis,

Zheng

et al. 2022

Human-Computer Interaction. Technological Innovation

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A Scoping Review of HRI Research on ‘Anthropomorphism’: Contributions to the Method Debate in HRI

Damholdt

Quick

Seibt

et al. 2023

Int J of Soc Robotics

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Anthropomorphism represents a central theoretical term in social robotics and human robot interaction (HRI) research. However, the research into anthropomorphism displays several conceptual problems that translate into methodological shortcomings. Here we report the results of a scoping review, which we conducted in order to explore (i) how the notion of ‘anthropomorphism’ is understood in HRI and social robotics research, and (ii) which assessment tools are used to assess anthropomorphism. Three electronic databases were searched; two independent reviewers were involved in the screening and data extraction process; a total of 57 studies were included in the final review which encompassed 43 different robots and 2947 participants. Across studies, researchers used seven different definitions of anthropomorphism and most commonly assessed the phenomenon by use of amended versions of existing questionnaires (n = 26 studies). Alternatively, idiosyncratic questionnaires were developed (n = 17 studies) which, as a qualitative thematic analysis of the individual questionnaire items revealed, addressed nine distinct themes (such as attribution of shared intentionality, attribution of personality etc.). We discuss these results relative to common standards of methodological maturity and arrive at the conclusion that the scope and heterogeneity of definitions and assessment tools of anthropomorphism in HRI hinders cross-study comparisons, while the lack of validated assessment tools might also affect the quality of results. To nurture reflection on these methodological challenges and increase comparability within the field we conclude by offering a set of reporting guidelines for research on anthropomorphism, as a first constructive effort to facilitate a coherent theory of anthropomorphism.

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The Effects of Robot Appearances, Voice Types, and Emotions on Emotion Perception Accuracy and Subjective Perception on Robots

Cited by 5 publications

References 12 publications

Facial Expressions Increase Emotion Recognition Clarity and Improve Warmth and Attractiveness on a Humanoid Robot without Adding the Uncanny Valley

Facial Expressions Increase Emotion Recognition Clarity and Improve Warmth and Attractiveness on a Humanoid Robot without Adding the Uncanny Valley

Theory of Mind Assessment with Human-Human and Human-Robot Interactions

A Scoping Review of HRI Research on ‘Anthropomorphism’: Contributions to the Method Debate in HRI

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